Evidence map›Paper›PMID 42444754›Full record

ArticleFrontiers in computational neuroscience2026

The neuroscience education readiness checklist for digital tools and methods.

Mathew Abrams, Michael Denker, Damien Depannemaecker, Joao Alves Ferreira, Maren Frings, Meysam Hashemi, Jakob Kaiser, Judith Kathrein, Wouter Klijn, Trygve B Leergaard and 6 more

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Mathew AbramsINCF Secretariat, Karolinska Institutet, Stockholm, Sweden.
Michael DenkerInstitute for Advanced Simulation (IAS-6), Jülich Research Center, Jülich, Germany.
Damien DepannemaeckerAix-Marseille Universite, UMR INSERM 1106 Institut des Neurosciences des Systèmes, Marseille, France.
Joao Alves FerreiraInstitute of Pharmacology and Experimental Therapeutics, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
Maren FringsForschungszentrum Jülich GmbH, Jülich Supercomputing Center (JSC), Simulation and Data Lab Neuroscience, Jülich, Germany.
Meysam HashemiAix-Marseille Universite, UMR INSERM 1106 Institut des Neurosciences des Systèmes, Marseille, France.
Jakob KaiserInstitute of Computer Engineering, Heidelberg University, Heidelberg, Germany.
Judith KathreinDepartment of Teaching and Study Organization, Medical University of Innsbruck, Innsbruck, Austria.
Wouter KlijnForschungszentrum Jülich GmbH, Jülich Supercomputing Center (JSC), Simulation and Data Lab Neuroscience, Jülich, Germany.
Trygve B LeergaardNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Milagros MarínDataJoint Inc., Houston, TX, United States.
Karyn OnyenehoNational Institutes of Health, Bethesda, MD, United States.
Maja A PuchadesNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Thomas WachtlerFaculty of Biology, Ludwig-Maximilians-Universität München, Munich, Germany.
Ekaterina ZossimovaForschungszentrum Jülich GmbH, Jülich Supercomputing Center (JSC), Simulation and Data Lab Neuroscience, Jülich, Germany.
Johannes PasseckerInstitute of Systems Neuroscience, Medical University of Innsbruck, Innsbruck, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of digital technologies in neuroscience, accompanied by a shift toward technology-enhanced learning environments, has created significant learning opportunities while simultaneously expanding the landscape of advanced training resources, ranging from biophysical simulations to high-dimensional neuroimaging and electrophysiological datasets. However, a critical "educator readiness gap" exists because the successful implementation of these tools often depends on the instructors' technical expertise and on their capacity to seamlessly integrate them into pedagogical practice. This paper introduces the Neuroscience Education Readiness Checklist (NERC), a 25-item self-assessment framework designed for creators and developers of neuroscience tools and training resources. NERC provides a structured approach that covers the major areas of concern in a comprehensive manner with actionable items. It organizes criteria into three overarching domains: general items and accessibility, instructor usability and workflow, and technical readiness. By addressing specific needs such as domain-specificity, pedagogical utility, and infrastructure requirements, NERC aims to reduce the burden on faculty members who evaluate training resource quality while providing important feedback for creators and developers. Developed through co-design workshops and refined with developer feedback, NERC provides a standardized approach to ensure neuroscience tools are not only technologically sound but also readily education-adoptable and scalable. While initial content has been curated through co-design, future validation will assess inter-rater reliability and predictive validity against adoption outcomes. Ultimately, NERC aims to foster a reproducible educational ecosystem, bridging the gap between innovative neuroscience and effective classroom implementation to ensure equitable access to high-quality neuroscience training.

Indexed as

assessmentchecklisteducationneurosciencetraining resource

Identifiers

PMID42444754
PMCPMC13357517

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.